Examples with LocalList uk.ac.sussex.gdsc.core.utils.LocalList used on opensource projects

Example 6 with LocalList

use of uk.ac.sussex.gdsc.core.utils.LocalList in project GDSC-SMLM by aherbert.

the class ImageJImagePeakResultsTest method addPeakResults.

private static void addPeakResults(ImageJImagePeakResults results, float[] x, float[] y, float[] value) {
    final LocalList<PeakResult> list = new LocalList<>(x.length);
    for (int i = 0; i < x.length; i++) {
        list.add(new PeakResult(x[i], y[i], value[i]));
    }
    results.addAll(list);
}

Example 7 with LocalList

use of uk.ac.sussex.gdsc.core.utils.LocalList in project GDSC-SMLM by aherbert.

the class PsfCombiner method createImageList.

/**
 * Creates the image list. Images must be greyscale, square and a stack of a single channel.
 *
 * @return the list
 */
public static List<String> createImageList() {
    final List<String> titles = new LocalList<>();
    final int[] ids = WindowManager.getIDList();
    if (ids != null) {
        for (final int id : ids) {
            final ImagePlus imp = WindowManager.getImage(id);
            if (imp != null && // Image must be greyscale
            (imp.getType() == ImagePlus.GRAY8 || imp.getType() == ImagePlus.GRAY16 || imp.getType() == ImagePlus.GRAY32) && // Image must be square and a stack of a single channel
            (imp.getWidth() == imp.getHeight() && imp.getNChannels() == 1) && // Check if these are PSF images created by the SMLM plugins
            containsPsf(imp)) {
                titles.add(imp.getTitle());
            }
        }
    }
    return titles;
}

Example 8 with LocalList

use of uk.ac.sussex.gdsc.core.utils.LocalList in project GDSC-SMLM by aherbert.

the class TraceExporter method exportVbSpt.

@SuppressWarnings("resource")
private void exportVbSpt(MemoryPeakResults results) {
    // vbSPT file format:
    // https://sourceforge.net/projects/vbspt/
    // Matlab matrix file (.mat) containing at least one variable that is a cell
    // array where each element, representing a trajectory, is a matrix
    // where the rows define the coordinates in one, two or three dimensions
    // in subsequent timesteps. The number of dimensions to be used for the
    // analysis will be set by the runinputfile.
    // The units are arbitrary but vbSPT starting estimates must be in the same
    // units. Either nm or μm are recommended.
    // 3 columns for n rows of localisations
    // 1. x coordinate (μm)
    // 2. y coordinate (μm)
    // 3. z coordinate (μm)
    // 
    // Note: An extra column is added containing the frame. This allows results to
    // be uniquely identified using frame,x,y,z
    // Count the IDs. Each new result ID will increment the count.
    final FrameCounter idCounter = new FrameCounter(results.getFirst().getId() - 1);
    results.forEach((PeakResultProcedure) result -> {
        if (idCounter.advance(result.getId())) {
            idCounter.increment();
        }
    });
    // Create the cell array as 1xN
    final Cell out = Mat5.newCell(1, idCounter.getCount());
    // This will reset the counter to zero and ensure the current frame does not match
    // in the event of a single track
    idCounter.advanceAndReset(idCounter.currentFrame() + 1);
    final boolean is3d = results.is3D();
    // Write the tracks
    final LocalList<double[]> list = new LocalList<>();
    results.forEach(DistanceUnit.UM, (XyzrResultProcedure) (x, y, z, result) -> {
        if (idCounter.advance(result.getId())) {
            addTrack(out, idCounter.getCount() - 1, list, is3d);
            idCounter.increment();
            list.clear();
        }
        list.add(new double[] { x, y, z, result.getFrame() });
    });
    addTrack(out, idCounter.getCount() - 1, list, is3d);
    try (MatFile matFile = Mat5.newMatFile()) {
        matFile.addArray("tracks", out);
        Mat5.writeToFile(matFile, Paths.get(settings.directory, results.getName() + ".mat").toFile());
    } catch (final IOException ex) {
        handleException(ex);
    }
}

Example 9 with LocalList

use of uk.ac.sussex.gdsc.core.utils.LocalList in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method analyseRois.

/**
 * Analyses all the ROIs in the ROI manager.
 *
 * @param event the event
 */
private void analyseRois(ActionEvent event) {
    final RoiManager manager = RoiManager.getInstance();
    if (manager == null) {
        IJ.error(TITLE, "ROI manager is not open");
        return;
    }
    final LocalList<Roi> rois = Arrays.stream(manager.getRoisAsArray()).filter(Roi::isArea).collect(LocalCollectors.toLocalList());
    if (rois.isEmpty()) {
        IJ.error(TITLE, "No area ROIs");
        return;
    }
    // Check for overlaps.
    if (!settings.getDisableOverlapCheck() && anyOverlap(rois)) {
        final GenericDialog gd = new GenericDialog(TITLE);
        gd.addMessage(TextUtils.wrap("WARNING - Bounding rectangles of ROIs overlap. You can verify " + "the ROIs on the image using the ROI manager 'Show all' function.", 80));
        gd.setOKLabel("Continue");
        gd.showDialog();
        if (gd.wasCanceled()) {
            return;
        }
    }
    // For each ROI:
    // - Extract the current groups
    // - Build the cumulative count plot
    // - Identify the bursts
    // - Extract ClusterData for each burst
    final TcPalmAnalysisSettings settings = this.settings.build();
    final LocalList<ClusterData> allClusters = rois.parallelStream().map(roi -> {
        final Rectangle2D scaledBounds = createScaledBounds(roi);
        final BiPredicate<ClusterData, Rectangle2D> filter = createSelectionFilter(roi, settings);
        // Filter all cluster groups
        final LocalList<ClusterData> clusters = new LocalList<>();
        clusterData.forEach(c -> {
            if (filter.test(c, scaledBounds)) {
                clusters.add(c);
            }
        });
        // Extract activation bursts
        final CumulativeCountData countData = createCumulativeCountData(clusters, false);
        final LocalList<int[]> bursts = runBurstAnalysis(settings, countData);
        final LocalList<LocalList<PeakResult>> burstLocalisations = createBurstLocalisations(clusters, bursts);
        clusters.clear();
        burstLocalisations.forEach(list -> {
            final ClusterData d = new ClusterData(clusters.size() + 1, list);
            // Save this for analysis
            d.sourceRoi = roi;
            d.getArea();
            clusters.add(d);
        });
        return clusters;
    }).collect(LocalList::new, LocalList::addAll, LocalList::addAll);
    // Reorder
    final Counter count = new Counter();
    allClusters.forEach(c -> c.id = count.incrementAndGet());
    // Display in a table
    final ClusterDataTableModelFrame frame = createAllClustersTable();
    frame.getModel().setData(allClusters, dataCalibration);
    // Allow the results to be repeated
    frame.selectedAction = clusters -> {
        // Expecting a single cluster. No clusters occurs when the table (and selection) is cleared.
        if (clusters.size() == 1) {
            final ClusterData c = clusters.get(0);
            // Push the correct ROI and settings to the analysis action.
            // We do not directly update the ROI or dialog settings as
            // these trigger events that are processed to add work with a delay.
            // Updating them at the end should generate events that are
            // ignored when finally executed as the ROI/settings should be the same.
            addWork(0, c.sourceRoi, settings, () -> {
                // When analysis has finished update the settings and image ROI.
                image.getImagePlus().setRoi(c.sourceRoi);
                darkTimeToleranceTextField.setText(Integer.toString(settings.getDarkTimeTolerance()));
                minClusterSizeTextField.setText(Integer.toString(settings.getMinClusterSize()));
                // When analysis has finished the cluster should be selected in the
                // current clusters table.
                final ClusterDataTableModelFrame currentClusters = currentClustersTable.get();
                if (currentClusters != null) {
                    currentClusters.select(c);
                }
            });
        }
    };
    // Show histogram of cluster size/duration
    reportAnalysis(settings, allClusters, dataCalibration);
    // Save clusters to memory
    final Trace[] traces = allClusters.stream().map(c -> {
        final Trace t = new Trace();
        t.setId(c.id);
        c.results.forEach(t::add);
        return t;
    }).toArray(Trace[]::new);
    TraceMolecules.saveResults(results, traces, "TC PALM");
    IJ.showStatus(TITLE + ": " + TextUtils.pleural(allClusters.size(), "cluster"));
}

Example 10 with LocalList

use of uk.ac.sussex.gdsc.core.utils.LocalList in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method createClusterData.

@SuppressWarnings("null")
private static LocalList<ClusterData> createClusterData(MemoryPeakResults results) {
    results.sort(IdFramePeakResultComparator.INSTANCE);
    final LocalList<ClusterData> clusterData = new LocalList<>();
    final FrameCounter counter = new FrameCounter(results.getFirst().getId() - 1);
    ClusterData data = null;
    final int size = results.size();
    for (int i = 0; i < size; i++) {
        final PeakResult result = results.get(i);
        if (counter.advance(result.getId())) {
            clusterData.add(data);
            data = new ClusterData(result);
        } else {
            data.add(result);
        }
    }
    // Final cluster
    clusterData.add(data);
    // Remove the first null object and compact the frame arrays
    clusterData.remove(0);
    clusterData.forEach(ClusterData::finish);
    // Sort by time then cluster ID
    clusterData.sort((c1, c2) -> {
        final int result = Integer.compare(c1.start, c2.start);
        if (result != 0) {
            return result;
        }
        // }
        return Integer.compare(c1.id, c2.id);
    });
    return clusterData;
}